Corsair C8 card delivers 9x faster performance than Nvidia’s H100 GPU in d-Matrix generative AI workloads

Specifically designed for ML and inference (Image source: d-Matrix)
Specifically designed for ML and inference (Image source: d-Matrix)

The Corsair C8 AI accelerator leverages the power of d-Matrix’s proprietary digital-in-memory computer (DIMC) technology. With 2,048 such DIMCs supported by 256 GB of LPDDR5 RAM and an additional 2 GB of SRAM, a single accelerator can reach speeds of up to 9600 TFLOPS.

With TSMC warning that Nvidia’s H100 AI GPUs will continue to see short supply over the next 18 months due to inadequate chip-on-wafer-on-substrate (CoWoS) packaging capabilities, other companies see an opportunity to step in and make a compelling case. The demand for generative AI hardware. One such company is a new startup called d-Matrix, which recently managed to secure US$110 million from investors like Microsoft by demonstrating the incredible speed of its Corsair C8 compute card that has 9x the generative throughput of the Nvidia H100 GPU. AI workloads.

d-Matrix’s Digital-in-Memory Computer (DIMC) technology achieves impressive speeds. Unlike Nvidia’s A100 and H100 GPUs that are designed for a wide variety of high-performance computing tasks, d-Matrix’s Corsair C8 cards are specifically designed for generative AI and inference workloads. A single Corsair C8 card combines 2,048 DIMC cores grouped into 8 chiplets with a total of 130 billion transistors and also features 256 GB of LPDDR5 RAM to easily fit full AI models into memory. All chiplets are interconnected via PCIe 5.0 lanes with 1 TB/s bandwidth resulting in a total die-to-die bandwidth of 8 TB/s. d-Matrix also includes 2 GB SRAM with 150 TB/s bandwidth. Depending on the AI ​​workload, the Corsair C8 delivers between 2400 and 9600 TFLOPS.

Thanks to optimized scaling capabilities and the frictionless nature of the open source software stack, d-Matrix claims that a cluster of Corsair C8 cards can achieve 20x better power efficiency, 20x lower latency and up to 30x lower costs than a similar solution from Nvidia. .

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